Original Research
29 June 2023
DOI 10.3389/fmars.2023.1133588
TYPE
PUBLISHED
OPEN ACCESS
EDITED BY
Leonie Esters,
University of Bonn, Germany
REVIEWED BY
Xianqing Lv,
Ocean University of China, China
Lequan Chi,
University of Georgia, United States
*CORRESPONDENCE
Yeon S. Chang
yeonschang@kiost.ac.kr
29 December 2022
06 June 2023
PUBLISHED 29 June 2023
Deconstructing the causes of
July sea level variability in the
East Sea from 1994 to 2021
MyeongHee Han 1, Yeon S. Chang 2,3* and Jeseon Yoo 1,4
1
Coastal Disaster & Safety Research Department, Korea Institute of Ocean Science and Technology,
Busan, Republic of Korea, 2 Maritime ICT & Mobility Research Department, Korea Institute of Ocean
Science and Technology, Busan, Republic of Korea, 3 Department of Ocean Science, University of
Science and Technology, Daejeon, Republic of Korea, 4 Department of Marine Technology and
Convergence Engineering, University of Science and Technology, Daejeon, Republic of Korea
RECEIVED
ACCEPTED
CITATION
Han M, Chang YS and Yoo J (2023)
Deconstructing the causes of July sea
level variability in the East Sea from
1994 to 2021.
Front. Mar. Sci. 10:1133588.
doi: 10.3389/fmars.2023.1133588
COPYRIGHT
© 2023 Han, Chang and Yoo. This is an
open-access article distributed under the
terms of the Creative Commons Attribution
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provided the original author(s) and the
copyright owner(s) are credited and that
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academic practice. No use, distribution or
reproduction is permitted which does not
comply with these terms.
In 2021 the East Sea experienced its highest July sea level (65.09 cm), as well as
the highest July sea surface and atmospheric temperatures, in the 29 years
between 1993 and 2021. We present several methodologies to identify the more
important causes of sea level change (SLC) in a semi-enclosed sea and explore
the critical fluctuation of ocean mass transport divergence during a period of
rapid sea level rise. Based on satellite altimeter data in the East Sea, the SLC, as
reflected in the absolute dynamic topography (ADT), which is the ADT difference
between the last and first days of a month, was 7.18 cm in July 2021. This may
reflect a combination of oceanic and atmospheric factors: ocean heat transport
divergence among the Korea, Tsugaru, and Soya Straits was found to contribute
2.32 cm of SLC, atmospheric heat flux at the sea surface contributed 2.87 cm of
SLC, and mass divergence (including errors) accounted for 1.98 cm of SLC. The
monthly mean sea level (ADT) variation in the East Sea should be examined in
terms of the oceanic and atmospheric fluxes, and the SLC sources can
additionally be divided into heat and others (mass + errors). The proportional
contribution of heat to SLC from 1994 to 2021 was 97.4%. Although the
contributions of mass and errors were small, there were substantial temporal
fluctuations, as their standard deviation reached up to 96.7% of that of SLC in
ADT. In the near future, a more precise analysis of the contributions of mass and
errors to SLC is required.
KEYWORDS
sea level, E ast S ea, he at transport, m ass transport, surface heat flux,
interannual variability
1 Introduction
For decades, measurements of sea level (SL) variability using in situ and remote
observations (Vivier et al., 1999; Calafat and Chambers, 2013) have contributed to the
understanding of ocean status and phenomena. For example, SL can be an indicator of
ocean warming associated with climate change as it changes due to the thermal expansion
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Han et al.
10.3389/fmars.2023.1133588
extraordinary SL variance in July 2021, by comparing SL values
observed in July of the previous years. To achieve this, we used
satellite absolute dynamic topography (ADT; daily reprocessed
allsat level 4) (CMEMS_portal, 2023) from the Archiving,
Validation and Interpretation of Satellite Oceanographic data and
the Copernicus Marine Environment Monitoring Service (AVISO/
CMEMS), as well as reanalysis data from the HYbrid Coordinate
Ocean Model (HYCOM) reanalysis and analysis (GOFS 3.1
GLBb0.08, HYCOM_GOFS_3.1_Analysis, 2023;
HYCOM_GOFS_3.1_Reanalysis, 2023) and the European Centre
for Medium-Range Weather Forecasts Reanalysis v5 (ERA5)
(ERA5_monthly_averaged, 2023). We believe this study is
valuable not only in terms of identifying the mechanisms that
cause unusual SL patterns in the ES at specific times but also for
understanding their relationship to long-term phenomena related
to climate change in this region. To the best of our knowledge, ours
is the first study to deconstruct the causes of sea level change (SLC)
in the ES.
The remainder of this paper is structured as follows. The
methods used to obtain the results are discussed in Section 2,
along with a background description of the mechanisms that may
contribute to SL variation. The results of the analyses are presented
in Section 3, and in Section 4 we discuss the results.
of seawater (Widlansky et al., 2020). SL data from satellite altimetry
or sea surface temperature (SST) have also been widely used to map
ocean surface currents through the construction of geostrophic
structures (Ducet et al., 2000; Gonzá lez-Haro et al., 2020).
Therefore, elucidating SL variability is essential for many
applications and analyzing the factors that cause variability will
contribute to a better understanding of ocean dynamics at the global
and regional levels.
SL fluctuates both spatially and temporally. The global mean SL
(MSL) has been increasing for more than a hundred years owing to
climate change (Church and White, 2011; Haigh et al., 2014;
Slangen et al., 2014). However, SL variability exhibits locality, as
variation rates that differ from the global rate have been observed at
regional scales (Katsman et al., 2008; Sterlini et al., 2016). Regional
SL also shows temporal variations ranging from seasonal to decadal
timescales (Kim and Yoon, 2010; Dangendorf et al., 2013).
Therefore, understanding the conditions that contribute to
temporal SL variation in specific parts of the ocean is critical for
predicting the effects of climate change in those regions.
Multiple factors contribute to these SL variations (Vivier et al.,
1999). The SL trends ± their standard deviations of total, mass, and
steric components in global MSL were 3.05 ± 0.24, 1.75 ± 0.12, and
1.15 ± 0.12 mm year-1 from 1993 to 2016, respectively (Horwath
et al., 2022). The mass increase due to the melting of glaciers and
polar ice caps has been shown to have raised the global SL
considerably (Jacob et al., 2012), and ocean thermal expansion is
one of the primary mechanisms underlying SL variability, both
globally (Widlansky et al., 2020) and regionally (Fasullo and Gent,
2017; Cazenave et al., 2018). SL variations have also been caused by
temporal variations in seawater temperature caused by interannualto-interdecadal phenomena, such as the Pacific Decadal Oscillation
(Casey and Adamec, 2002), or by shorter seasonal heating of ocean
surface water (Leuliette and Wahr, 1999). Other mechanisms
include atmospheric forcing due to pressure, such as the North
Atlantic Oscillation (Wakelin et al., 2003; Yan et al., 2004), and
wind forcing by planetary-scale surface winds, such as Rossby waves
(Qiu and Chen, 2006). In addition to the near-annual period of
Rossby waves, Ekman pumping via wind stress curl contributes to
local SL fluctuation (Vivier et al., 1999; Xu and Oey, 2015).
In this study, we analyzed the factors that contributed to SL in
the East Sea (ES) in July from 1994 to 2021 and focused on July 2021
when there was the highest July MSL from 1993 to 2021, as
measured by satellite altimeters. The ES is a semi-enclosed sea
bounded by the Korean Peninsula and the Japanese Islands, with a
relatively shallow and narrow entrance (Korea Strait; KS) through
which a branch of the Kuroshio Current flows into the ES and exits
via the Tsugaru and Soya Straits (TS and SS) and into the Sea of
Okhotsk and the North Pacific. Therefore, the difference in seawater
transports among these straits can cause SL variation. Furthermore,
the ES, being located in the mid-latitudes, exhibits clear seasonal
variation in seawater temperature, resulting in a high SL in summer
and low SL in winter (Han et al., 2020a). Unlike that in the open
ocean, the SL in the ES can be influenced by a variety of factors,
including the mechanisms described above.
The aim of this study was to analyze the factors that contributed
to the SL variances over 29 years and, in particular, the
Frontiers in Marine Science
2 Materials and methods
2.1 Background
2.1.1 Study site
The ES is a marginal sea enclosed by the Korean Peninsula and
the Japanese Islands, ranging from 35°N to 52°N and 127°E to 143°
E (Figure 1). The KS, which has a width of approximately 160 km,
connects to the East China Sea at the southern end (Teague et al.,
2002), through which the Tsushima Current, a branch of the
Kuroshio Current, enters the ES. The inflow water is divided into
two main branches: one flowing north along the east coast of the
Korean Peninsula and the other flowing northeast along the west
coast of the Japanese Islands. Both branches circulate and meander
within the ES, mostly in the southern ES, before flowing into the
North Pacific and the Sea of Okhotsk through the TS and SS (Han
et al., 2020a; Han et al., 2020b).
2.1.2 Sea level observation in the ES
There is a marked seasonal variation of SL in the ES, with high
values observed in summer and fall and low values observed in
winter and spring due to the steric effect of seawater, mass exchange
among the atmosphere and the three straits, and atmospheric
pressure differences; the average SL variation is 30.4 cm between
summer and winter from 1993 to 2018 (Han et al., 2020a).
Furthermore, SL has gradually increased in the ES for decades
owing to climate change, with an average increase rate of 3.78 mm
year-1 from 1993 to 2017 (Han et al., 2020b). In addition to these
seasonal variations and decadal trends in SL, an extreme value of the
monthly MSL (from ADT measures) was recorded in July 2021; the
SL was found to be 65.09 cm, which was the highest compared with
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10.3389/fmars.2023.1133588
A
B
FIGURE 1
(A) Monthly mean sea level (MSL) in absolute dynamic topography (ADT) (MSLADT ) in the East Sea (ES) in July 2021 and (B) sea level change (SLC) in
ADT (SLCADT ) from July 1 (00:00 UTC) to August 1 (00:00 UTC) in 2021 (ADT difference between 00:00 UTC August 1 and 00:00 UTC July 1 in
2021). The iso-ADT interval is 5 cm, and coastlines are drawn in gray. KOR, RUS, and JPN indicate Korea, Russia, and Japan, respectively. KS, TS, and
SS denote Korea, Tsugaru, and Soya Straits, respectively. The ES is represented by a red solid rectangle on the map (upper left in (A)).
than seawater exiting from the TS/SS. Therefore, the temperature
difference between the KS and TS/SS can increase the temperature
of the ES, which is then released from the ocean into the atmosphere
via the sea surface. If we assume that the heat released into the
atmosphere is constant over time (although this is not the case, as
shown in Figure 2B), the seawater heat difference anomaly between
the KS and TS/SS can contribute to the SL variation in the ES due to
thermal expansion and contraction of the upper layer of the
seawater. In contrast, if the difference in heat transport between
the KS and TS/SS is assumed to be identical (although this is not
actually the case, the SL in the ES would increase or decrease in
response to changes in the surface net incoming heat flux, reflected
in the corresponding expansion and contraction of the upper layer
of water in the ES.
Another consideration is the changes in mass transport. As
shown in Figure 1, a branch of the Kuroshio Current flows into the
ES via the KS, and seawater flows out via the two narrower straits,
TS and SS, because the mass transport through the Tatarsky Strait
and other gaps between the ES and surrounding waters can be
negligible (Hirose et al., 1996; Lyu et al., 2002). Therefore, the
difference in mass transport among these straits can affect the SL of
the ES. Furthermore, the length of time that seawater stays in the ES
after entering through the KS could be prolonged when the
pathways of the main currents are changed by the formation of
mesoscale eddies, which leads to an increased residence time
of these currents in the ES. Therefore, the extended residence
time of the incoming water could be one of the reasons the SL is
raised when the incoming mass transport through the KS exceeds
the outgoing mass transport through the TS/SS. In the present
study, we examined the abovementioned factors to determine what
led to the extreme SL values in July 2021.
previous July measurements of SL from 1993 to 2021. We present in
Figure 1A) the monthly mean of the ADT in July 2021 contoured in
the ES with a 5-cm interval, as measured by satellites. The ADT was
high, particularly in the southern part of the ES, where mesoscale
eddies were formed, and the iso-ADT lines fluctuated considerably
around the eddies (Figure 1A). We calculated the ADT difference
between the last and first days of July to eliminate the effect of
previous months (Figure 1B). We found that the increase in ADT
was higher in the southern ES than in the northern ES. As shown in
Figure 2, the monthly mean ADT, atmospheric temperature (AT) at
2 m above the sea surface, and SST are compared in July of each
year from 1993 to 2021. The ADT, AT, and SST reached their
highest values in July 2021 (65.09 cm, 23.04°C, and 22.00°C,
respectively). Thus, the highest recorded SL in July 2021 could be
attributed to extreme values of heat flux from the atmosphere into
the ocean and seawater temperature, as well as the rising SL trend
due to global warming. In this study, we focused on the July ADT in
2021, as well as corresponding measures of ADT over the 29 years,
and examined the factors that contributed to this unusually
large increase.
The authors of previous studies (Pinardi et al., 2014; Han et al.,
2020a; Han et al., 2020b) have established that increasing the heat
and mass in a semi-enclosed sea, such as the ES, raises the SL. For
example, SL increases and decreases with the rise and fall in AT and
SST if the effects of mass and atmospheric pressure on SL are
negligible. The temperature of the water that flows into the ES
through the KS is higher than that of the water in the ES, generally,
and the water that flows through the TS/SS, because the water in the
KS originates from lower latitudes. If we assume that the mass
transport flows into and out of the ES are identical (although this is
not actually the case, the seawater entering from the KS is warmer
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A
B
FIGURE 2
(A) July MSL in ADT (MSLADT , m, black filled diamond, left y-axis), air temperature (°C, at 2 m above the sea surface, blue open circle, right y-axis),
and sea surface temperature (°C, at 0 m, red open square, right y-axis) from 1993 to 2021. The solid lines represent their linear trends. (B) July
atmospheric net heat flux (AH, W m-2, positive means downward, blue-filled square), July shortwave radiation flux (SWR, W m-2, positive means
downward, red open circle), July longwave radiation flux (LWR, W m-2, positive means downward, green open upward triangle), July latent heat flux
(LHF, W m-2, positive means downward, magenta hexagon), and July sensible heat flux (SHF, W m-2, positive means downward, cyan open
downward triangle) from 1993 to 2021.
where MSLADT , ADTD , and ND indicate the monthly MSL as
reflected in ADT (m), daily ADT (m), and number of days in a
month, respectively. The MSL of the ES was calculated using area
weighting because the longitudinal angular distance decreases as the
latitude increases.
2.2 Data and methods
The CMEMS dataset provides ADT of a 0.25° horizontal grid
from all satellite altimeter missions, in two formats: near-real time
and reprocessed data. We analyzed the reprocessed level 4 daily
ADT (CMEMS_portal, 2023) in the ES in July over 29 years (1993
to 2021) to calculate the monthly MSL (for example, July MSL is the
mean of daily ADT values from July 1 to July 31) and monthly sea
level change (SLC; for example, July SLC is calculated by subtracting
ADT at 00:00 UTC on July 1 from ADT at 00:00 UTC on August 1)
as follows:
MSLADT =
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o​ ADTD
ND
SLCADT = ADTL − ADTF = SLCAH + SLCOH + SLCME
(2)
where SLCADT represents the monthly SLC as reflected in ADT
within a month (m). ADTF and ADTL represent the daily ADT values
on the first (e.g., 00:00 UTC on July 1) and last (e.g., 00:00 UTC on
August 1) days of the month (m). SLCAH represents the monthly SLC
in Atmospheric sea surface net Heat flux (AH, m) in Eq. (3). SLCOH
represents the monthly SLC in Oceanic lateral Heat transport
divergence (OH, m) among the KS, TS, and SS in Eq. (4). SLCME
(1)
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10.3389/fmars.2023.1133588
HYCOM_GOFS_3.1_Reanalysis, 2023) were also used to calculate
SLCOH , as shown in Eq. (4):
represents the monthly SLC in oceanic Mass transport divergence
including other Errors (ME) in Eq. (5). Therefore, SLCADT was
obtained by calculating the difference between the daily ADT
values of the last and first days of the month (ADTL − ADTF ). This
difference is equal to the sum of the monthly SLC value attributed to
atmospheric heat, oceanic heat, and oceanic mass including other
errors (SLCAH + SLCOH + SLCME ). To calculate a mixed layer depth
at which the water temperature first drops by 0.02°C below the SST
(Cummings and Smedstad, 2013), we analyzed the daily mean SST
from the Optimum Interpolated Sea Surface Temperature dataset
(OISST; ver. 2; obtained from the National Oceanic and Atmospheric
Administration [NOAA] National Centers for Environmental
Information [NCEI] (NCEI_OISST, 2023)), which consists of data
in a horizontal 0.25° grid (Reynolds et al., 2007), and the monthly
mean water potential temperature, salinity, and water potential
density from HYCOM global reanalysis and analysis of a
horizontal 0.08° grid (GOFS 3.1 GLBb0.08,
HYCOM_GOFS_3.1_Analysis,
2023;
HYCOM_GOFS_3.1_Reanalysis, 2023). We examined the monthly
mean ERA5 data (ERA5_monthly_averaged, 2023) of a horizontal
0.25° grid published by the European Centre for Medium-Range
Weather Forecasts (ECMWF). These ERA5 data include sea surface
shortwave radiation flux (SWR), longwave radiation flux (LWR),
latent heat flux (LHF), sensible heat flux (SHF), and AT at 2 m above
the sea surface, which can affect the SL of the ES (Hersbach et al.,
2020). The thermal expansion of the SLC (SLCAH [m]) caused by
atmospheric sea surface net heat flux (QAH [W m-2]; sum of SWR,
LWR, LHF, and SHF from the atmosphere to ocean) was calculated
as follows:
Z
SLCAH =
Z
yN
xE
dy
yS
Z
xW
Z
yN
=
xE
dy
yS
xW
Z
=½
Z
aKS vKS (TKS − TREF ) dx
xW
Z
0
−DTS
Z
xE
dz
−DKS
−
: AtES
Z
0
−
yN
dz
yS
Z
0
−DSS
aTS uTS (TTS − TREF ) dy
yN
dz
yS
aSS uSS (TSS − TREF ) dy:
(4)
t
AES
where OHKS−TS−SS , vKS , uTS , and uSS represent the OH among
the KS, TS, and SS (J s-1); meridional velocity in the KS (m s-1); and
zonal velocities in the TS and SS (m s-1), respectively. TKS , TTS , TSS ,
TREF , aKS , aTS , and aSS represent water potential temperatures in
the KS, TS, and SS (°C), reference temperature which we take to be
0°C, and thermal expansion coefficients in the KS, TS, and SS (°C-1),
respectively. DKS , DTS , DSS , and z represent depths in the KS, TS,
and SS (m) and depth (m), respectively. The first term of the last
right-hand side of Eq. (4) is the SLC caused by the vertical sectional
integral of incoming heat transport that passes through the KS into
the ES, and the second and third terms are the SLCs caused by the
vertical sectional integrals of outgoing heat transports that pass
through the TS and SS out of the ES, respectively. SLCME is
calculated as shown in Eq. (5)
SLCME = SLCADT − SLCAH − SLCOH
(5)
The SLCME in Eq. (5) is the SLC attributed to oceanic mass
transport divergence including other errors and is computed by
subtracting the SLCs attributed to the atmospheric and oceanic net
heat fluxes from the SLC in ADT.
aQAH
t
dx:
AES
rCP
a(QSWR + QLWR + QLHF + QSHF )
t
dx:
rCP
AES
(3)
3 Results
where QSWR , QLWR , QLHF , and QSHF represent SWR, LWR,
LHF, and SHF, respectively, all reported in W m-2. AES , a, CP , r,
and t represent the sea surface horizontal area in the ES (~1012
m2), thermal expansion coefficient for seawater (°C-1), specific
heat capacity for seawater (~4,000 J kg-1°C-1), water potential
density (kg m-3) at the middle depth between the surface and
mixed layer depth, and time (a month in seconds), respectively. x,
xW , xE , y, yS , and yN represent longitude, the westernmost and
easternmost points, latitude, and the southernmost and
northernmost points (°), respectively. The SLCAH in Eq. (3) is
the sea surface integral of vertical contraction/expansion of the sea
water caused by surface net heat flux from the atmosphere to
ocean in the ES.
The monthly mean horizontal water velocity with water
potential temperature, salinity, and water potential density from
HYCOM global reanalysis and analysis of a horizontal 0.08° grid
(GOFS 3.1 GLBb0.08, HYCOM_GOFS_3.1_Analysis, 2023;
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aOHKS−TS−SS
rCP
SLCOH =
3.1 Atmospheric heat flux
As described in Section 2.1.2, one of the main mechanisms that
may contribute to SL elevation in the ES is thermal expansion
caused by excessive incoming heat into the water, as illustrated in
Figure 2A), which shows the SST and AT at 2 m above the sea
surface compared with the MSLADT calculated using Eq. (1) in July
for 29 years. SST and AT were both significantly higher in 2021
(23.04°C and 22.00°C, respectively) than in the previous years. In
contrast, the wind speed in July 2021 was less than 2 m s-1, which
was lower than the average of the previous years (not shown). The
results in Figure 2A) (SST and AT) indicate that thermal expansion
may be a major cause of the high SL in July 2021, whereas the low
wind speed could indicate the possibility of high atmospheric
pressure and/or no strong atmospheric pressure gradients in the
ES during the corresponding month.
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10.3389/fmars.2023.1133588
cm yr-1, respectively (Figure 3A). The MSLs in 2021 (June, July, and
August) are the maxima from 1993 to 2021, except for the MSL in
May, because the MSLsADT have increasing trends (global warming).
During the 29-year time period, the highest MSLADT in July (2021)
may have been caused by the highest MSLsADT in June (see
Figure 3A). We calculated the SLC for each month (SLCADT ) using
Eq. (2), as shown in Figure 3B), to investigate the net monthly SLC
without the influence of previous months. The SLCsADT in 2021 were
as follows (in cm): −2.60 (February), −1.42 (March), 5.13 (April),
−1.10 (May), 8.83 (June), 7.18 (July), and −5.94 (August) (Figure 3B).
The SLCsADT in June (8.83 cm) and July (7.18 cm) in 2021 were the
highest SLC in June and fifth highest in July, respectively, for the 29year period (1993–2021). Thus, the highest MSLADT in July 2021
(Figure 2A) is due to the SLCsADT in June and July 2021 (sum of red
To investigate this possibility, the monthly mean data of the
AH, SWR, LWR, LHF, and SHF were estimated using ERA5; the
July data are compared in Figure 2B). In the 29-year dataset, SWR
(235.62 W m-2, red open circle) was at its highest in July 2021. In
contrast, no discernible anomalies were observed in the AH, LWR,
LHF, and SHF values in 2021. The significant SWR in 2021
indicates that incoming solar radiation into seawater increased in
the corresponding month, most likely due to clear weather with
little cloud cover, resulting in higher SST and AT, as shown in
Figure 2A). This is an unusual occurrence considering that July is
typically the rainy season on the Korean Peninsula.
We compared the July MSLADT using Eq. (1) with the MSLsADT
in May, June, and August, as shown in Figure 3A). The monthly MSL
trends in May, June, July, and August were 0.40, 0.43, 0.44, and 0.49
FIGURE 3
(A) Monthly MSL in ADT (MSLADT ) in May (black open diamond), June (red open circle), July (green triangle), and August (blue square) from 1993 to
2021. The dashed lines represent their trends. (B) SLC in ADT (SLCADT ) in February (yellow), March (magenta), April (cyan), May (black), June (red),
July (green), and August (blue) from 1993 to 2021.
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estimated from May to August, given that the highest MSLADT (July
2021) was primarily controlled by atmospheric and oceanic net heat
fluxes (SLCAH and SLCOH ) in June and July.
In the 29-year data period, the highest July MSLADT occurred in
2021 (black-filled diamond in Figure 2A) due to the trend (0.44 cm
year-1, Figure 2A), the high SLCADT in June (8.83 cm; Figure 4E,
black open diamond in Figure 5), and the high SLCAH (2.35 and
2.87 cm in June and July; blue open square in Figure 5) and SLCOH
(2.08 and 2.32 cm in June and July; green open triangle in Figure 5)
in June and July 2021. Thus, the highest July MSLADT , in 2021, was
the result of the MSLADT in the previous month (June), along with
the SLC values affected by oceanic and atmospheric effects. From
1994 to 2021, the mean ± standard deviation of the SLCOH was 1.97
± 0.22 cm, and the relative contribution of SLCOH to SLCADT was
43.06 ± 8.02% (Figure 6). The standard deviation of SLCME is 2.70
cm and 96.73% (2.70 cm/2.79 cm) of that of SLCADT from 1994 to
2021, which means that the variance of ocean mass
transport divergence including other errors is large and similar to
that of SLCADT .
and green bars in 2021 in Figure 3B). The MSLADT in July in the ES is
a cumulative consequence of the water volume, both during the
previous month and in July itself; therefore, the SLCADT of the
previous month can affect the MSLADT of the next month in a
semi-enclosed basin such as the ES. Additionally, the highest MSLADT
in August 1994 excluding the trend (the height from the dotted blue
line to blue open square in Figure 3A) is the sum of the SLCsADT in
June, July, and August of that year (sum of red, green, and blue bars in
1994 in Figure 3B) because SLCADT in May is sufficiently negative to
cancel out the positive SLCsADT in March and April.
There are two sources of SLC in the ES: the atmosphere and the
ocean. The sources of heat and mass from the atmosphere are the
AH and precipitation minus evaporation, respectively. If we ignore
the effects of precipitation minus evaporation on SL in the ES, we
can compute the atmospheric effect on SL (SLCAH ) based on the AH
using Eq. (3). Based on HYCOM and ERA5 reanalysis data, the
contribution of AH to SL in 2021 can be determined by the thermal
expansion of seawater from the surface to the mixed layer depth in
May (Figure 4B), June (Figure 4F), July (Figure 4J), and August
(Figure 4N). The SLCAH can be averaged in the ES in May, June,
July, and August 2021 (blue open square in Figure 5) and can be
extended from 1994 to 2021 as July SLCAH to compare them only in
July during the observational period in Figure 6. Regardless of the
SLCAH values in June (2.35 cm) and July (2.87 cm) in 2021 in
Figures 4F, J), its direct contribution to the SLCADT in July 2021 was
estimated to be less than 3 cm. Therefore, the thermal expansion
caused by the increased heat transfer from the atmosphere to the
ocean may not be a controlling factor for the extreme MSLADT in
the ES in July 2021 as well as for the MSLsADT from 1993 to 2021;
thus, additional factors need to be considered. From 1994 to 2021,
the mean ± standard deviations of the SLCADT and SLCAH were 4.57
± 2.79 and 2.48 ± 0.27 cm, respectively, and the ratio of SLCAH to
SLCADT was 54.32 ± 9.84% (Figure 6).
4 Discussion
In this study, we have investigated several factors that possibly
contributed to the extreme MSLADT (65.09 cm) in July 2021, as well
as the atmospheric and oceanic factors affecting July MSLADT , in
general, over the 29 years of the dataset. The SLCADT in July 2021, as
estimated using satellite observations, was approximately 7.18 cm,
which was the fifth highest among July observations over 29 years
(Figure 3). The SLCADT in June was approximately 8.83 cm, which
was the highest June SLCADT over the 29-year period (Figure 3) and
affected the MSLADT in July because the latter included the former.
Although both SST and AT increased significantly—likely due to
mostly clear skies, which caused the unusually high SWR level in the
corresponding month—the SLCAH was estimated to be 40.0% of S
LCADT in July 2021, which is less than the average value (54.32%)
over the 29 years. In July, the atmospheric source of sea level change
(SLCAH ) was the highest among the sources of sea level change
(SLCAH , SLCOH , and SLCME ). However, the sources from others
(SLCOH and SLCME ) accounted for approximately 60.0% of SLCADT
in July 2021.
One of the major factors that contributed to the MSL peak in
July 2021 may have been the increase in the mass transport
divergence including errors (SLCME , 4.40 cm) in June in the ES.
Owing to the unusual atmospheric phenomena of high MSL
atmospheric pressure patterns in the northwestern Pacific, the
Ekman transport into the ES caused by wind stress anomaly and
its curl distribution around the ES may have contributed to an
increase in the seawater that enters the ES via the KS but decrease in
the seawater that exits the ES through the TS and SS (not shown),
resulting in an increase in the seawater and ADT in the ES.
Additional evidence of this process can be found in the MSLADT
distribution shown in Figure 1A, where several mesoscale eddies
developed in the southern part of the ES and the iso-ADT lines
showed strong meandering around the eddies. Enhancement of the
meandering of the currents and formation of more eddies could
3.2 Ocean heat transport
We present in Figure 4 the horizontal distributions of the
monthly (May, June, July, and August) values of SLCADT (Eq.
(2)), SLCAH (Eq. (3)), SLCADT − SLCAH ( = SLCOH + SLCME , Eqs.
(4) and (5)), and MSLADT from satellite data (Eq. (1)). In contrast to
the flat horizontal SLCAH , the SLCADT − SLCAH resembles the total
SLCADT . Values for SLCADT − SLCAH were estimated to be 6.48 cm
(June) and 4.30 cm (July) in 2021, which were 2.8 (6.48 cm/2.35 cm)
and 1.5 (4.30 cm/2.87 cm) times larger than the SLCsAH in June and
July, respectively.
In addition to these results, we calculated the SLCOH during
May, June, July, and August among the KS, TS, and SS using the
HYCOM reanalysis and analysis data (Figure 5). Accordingly, in
June and July 2021, the values of SLCOH were 2.08 and 2.32 cm
(green open triangle, Figure 5), respectively. Thus, there was more
heat from incoming seawater in July than in June. We found that
the highest MSLADT , which occurred in July 2021 (65.09 cm), was
caused not only by SLCOH in July (2.32 cm) but also by SLCOH in
June (2.08 cm), because seawater can accumulate in a semi-enclosed
sea over time. Furthermore, the data shown in Figure 5 were only
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Han et al.
10.3389/fmars.2023.1133588
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
FIGURE 4
Horizontal SLC in ADT (SLCADT ) in (A) May, (E) June, (I) July, and (M) August in 2021. Horizontal SLC in AH (SLCAH ) in (B) May, (F) June, (J) July, and
(N) August 2021. SLC in ADT minus SLC in AH (SLCADT − SLCAH = SLCOH + SLCME ) in (C) May, (G) June, (K) July, and (O) August 2021. MSL in ADT
(MSLADT ) in (D) May, (H) June, (L) July, and (P) August 2021.
be applied within this relatively small-sized marginal sea, although
the resolution is sufficiently fine to be applied to the global ocean.
Another possibility for the uncertainty might be the regridding. The
HYCOM output was interpolated in the new grids that were
different from the original modeling grids in the ES, which might
have increased the error. In addition, the velocity and density data
were assimilated in HYCOM. Therefore, small changes in these data
could have a significant impact, especially in calculating the mass
make the seawater remain in the ES, with a longer residence time,
resulting in increased SLCME , SLCADT , and MSLADT .
In calculating SLCME , the right-hand side of Eq. (5), SLCADT −
SLCAH − SLCOH , was used instead of using reanalysis data, namely,
HYCOM, because of the uncertainty of the reanalysis data. In the
case of HYCOM, for example, the accuracy of mass transport
divergence calculation might not be satisfactory in the ES,
because the horizontal resolution of 0.08° could be too coarse to
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FIGURE 5
SLC in ADT (SLCADT , black open diamond), AH (SLCAH , blue open square), and OH (SLCOH , green open triangle) in July from 1993 to 2021.
The increase in the input mass of seawater through KS might
induce an additional increase for the MSLADT in the ES if precipitation
minus evaporation plus runoff is negligible. Because the KS is located
at the southwestern end of the ES, the seawater that enters into the ES
is a part of the branch of the Kuroshio Current, Tsushima Warm
Current, and had a higher temperature than that of the seawater in the
ES. Therefore, the increase in mass transport in the KS is followed by
an increase in heat and mass into the ES. Alternatively, the decrease in
output mass transport through TS and SS might induce heat and mass
increases in the ES since the heat and mass could stay longer in the ES.
This impact of heat transport was significant, as the estimated SLCOH
between the KS and TS/SS was 2.32 cm, accounting for approximately
transport divergence, compared with the other parameter
(heat transport divergence) evaluated in this study. Owing to
these reasons, the final output did not satisfy the mass or
volume balance within the ES as the mean ± standard deviation
of the SLC based on ocean mass transport divergence among straits
calculated from HYCOM was 5.56 ± 5.36 cm, which is larger than
that of the SLCADT (4.57 ± 2.79 cm) over the 29 years. Thus, we have
used SLCME in Eq. (5), not directly calculation from HYCOM. In
addition, SLCME could have errors from satellite ADT data which
were interpolated spatially using tracked satellite data, from sea
surface heat flux data of ERA 5 reanalysis data, and from ocean heat
transport of HYCOM reanalysis data.
FIGURE 6
SLC in ADT (SLCADT , black open diamond), AH (SLCAH , blue open square), and OH (SLCOH , green open triangle) in May, June, July, and August 2021.
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SLCADT , SLCAH , and SLCOH : Only SLCAH and EASMI, SLCOH and
EASMI, and SLCOH and PDO were significantly correlated
(correlation coefficients (R): −0.47 (p-value = 0.01), −0.52 (p-value
= 0.00), and −0.37 (p-value = 0.05) (Figures 7 and 8). The first and
second correlation coefficients indicate that the effects of thermosteric
sea level fluctuations from the atmosphere and surrounding seas and
ocean into the ES (SLCAH and SLCOH ) are related to the East Asian
summer monsoon (EASM). The EASMI was calculated with a mean
zonal wind at 200 hPa as it is related to horizontal vorticity and
vertical motion of the air (Zhao et al., 2015). These air motions are
related to precipitation, especially the mei-yu–changma–baiu rainfall
in July, which could release condensational heat in the atmosphere
(Zhao et al., 2015), and to the subtropical ocean and north Pacific SST
(Lee et al., 2008). Thus, thermosteric SLC caused by the atmospheric
and oceanic heat (SLCAH and SLCOH ) related to the EASM changes
with EASMI significantly as showed in correlation coefficients above.
In addition, the thermosteric SLC caused by heat transport
divergence from the surrounding seas and ocean into the ES
(SLCOH ) is related to the PDO negatively, which means the sea
level rises when the PDO is negative. The SST anomaly in the western
Pacific is positive when the PDO is negative, indicating that there is
32.3% of the SLCADT in July 2021. Also, the increase in SLCAH was
2.87 cm, accounting for approximately 40.0% of SLCADT in July 2021
(Table 1). Therefore, our findings suggest that atmospheric and
oceanic heat fluxes may be the two major mechanisms underlying
SL variation in the ES. However, the factors that influence ocean mass
transport remain unclear. The unusually high and low atmospheric
pressure anomalies in the ES and East China Sea, respectively, in July
2021 may be one of the controlling factors for the MSLADT ; however,
this is not always the case in other years. In the ES, rainy season usually
begins in late June or early July, and generally, there is low
atmospheric pressure. Therefore, the existence of high atmospheric
pressure in July 2021 was unusual, and future research may be
required to investigate the relationship between atmospheric
pressure, rainfall, and the SL pattern in the ES during this season.
Climate indices, such as the Arctic Oscillation Index (AOI, Rigor
et al., 2002), East Asian Summer Monsoon Index (EASMI, Zhao et al.,
2015; Huang and Zhao, 2019), North Pacific Gyre Oscillation
(NPGO, Di Lorenzo et al., 2008), North Pacific Index (NPI,
Trenberth and Hurrell, 1994)), Oceanic Niño Index (ONI, Hafez,
2016), and Pacific Decadal Oscillation (PDO, Di Lorenzo et al., 2008),
in July from 1994 to 2021 were used to compare the variabilities in
TABLE 1 SLCs in ADT (SLCADT ), AH (SLCAH ), OH (SLCOH ), and ME (SLCME ), and MSL in ADT (MSLADT ) in May, June, July, and August 2021.
May
June
July
August
SLCADT (cm)
−1.10
8.83
7.18 (100.0%)
−5.94
SLCAH (cm)
1.57
2.35
2.87 (40.0%)
0.84
SLCOH (cm)
1.94
2.08
2.32(32.4%)
1.84
SLCME (cm)
−4.61
4.40
1.98 (27.6%)
−8.62
MSLADT (cm)
54.29
58.48
65.09
66.70
SLC, sea level change; ADT, absolute dynamic topography; AH, atmospheric sea surface net heat flux; OH, oceanic lateral heat transport divergence; MSL, monthly mean sea level; ME, oceanic
mass transport divergence including other errors.
FIGURE 7
SLC in AH (SLCAH , blue open square, left Y-axis) and OH (SLCOH , green open triangle, left Y-axis), and the East Asian Summer Monsoon Index
(EASMI; black open circle, right Y-axis) in July from 1994 to 2021. The correlations between SLCAH and EASMI and between SLCOH and EASMI are
−0.47 (p-value = 0.01) and −0.52 (p-value = 0.00), respectively.
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FIGURE 8
SLC in OH (SLCOH , green open triangle, left Y-axis) and Pacific Decadal Oscillation (PDO; black open square, right Y-axis) in July from 1994 to 2021.
The correlation coefficient between SLCOH and PDO is −0.37 (p-value = 0.05).
The causes of SL variation in a semi-enclosed sea are more
complex than those in an open ocean. The thermosteric effect of
seawater caused by the ocean and atmosphere is closely related to
SLC in a semi-enclosed sea. The SLC values in Table 1 show that
heat contributed 72.4% in 2021. From 1994 to 2021, the percentage
of SLC caused by heat was 97.4% (Figure 9). The mean ± standard
deviation of the SLCME was 0.12 ± 2.70 cm, and the relative
higher heat in the seas and ocean around the ES than that when the
PDO is not negative. Consequently, When the PDO is negative, the
SST in the western Pacific rises above average and the Kuroshio
weakens, allowing the strong Tsushima Warm Current to enter the
ES to a greater degree. As a result, the thermosteric SLC (SLCOH ) in
the ES increases when the PDO is negative (Gordon and Giulivi,
2004; Han and Huang, 2008; Andres et al., 2009).
FIGURE 9
Schematic of the sources of SLC in July, from 1994 to 2021 in the ES. Relative contribution of atmospheric and oceanic heat was 54.3% and 43.1%,
respectively, and heat and other sources (mass + errors) accounted for 97.4% and 2.6%, respectively.
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Simulation Technology for Maritime Spatial Policy), (20210607,
Establishment of the Ocean Research Station in the Jurisdiction
Zone and Convergence Research), and (20220033, Assessment and
Long-Term Projections of Ocean Climate Change)].
contribution of SLCME to SLCADT was 2.62 ± 96.73%, which shows a
severe undulation of the ocean mass transport divergence in the ES
including other errors. Seawater can accumulate and remain in a
semi-enclosed sea over time; thus, previous SLC due to mass and
heat (e.g., 1 month before) can influence future SLs (e.g., 1 month
later). Therefore, to understand the SL and its warming trend, we
must take the previous mass and heat fluxes into consideration.
Furthermore, the SL variation in the ES is related to the EASM and
PDO, indicating the influence of regional and global climate change
at the local sea level in a warming climate. In this study, we
proposed several methods for deconstructing the causes of SLC in
a semi-enclosed sea during a period of rapid SL rise. Our results
provide a foundation for future SLC research in this area; for
example, to understand SLC well, precise mass calculation from
the atmosphere, the land, and the ocean to the ES should be
investigated, as well as error computation of heat from the
atmosphere and the ocean to the ES in the near future.
Acknowledgments
Satellite altimetry data (SSALTO/DUACS altimeter products)
produced and distributed by the CMEMS (http://
www.marine.copernicus.eu), OISST data from the NOAA
Physical Science Laboratory (https://psl.noaa.gov/data/gridded/
data.noaa.oisst.v2.highres.html), and surface heat flux data from
the ECMWF Reanalysis v5 (http://www.ecmwf.int/) were used in
this study. The National Ocean Partnership Program and Office of
Naval Research have provided funding for the development of
HYCOM. Data assimilation products using HYCOM are funded
by the U.S. Navy. Computer time was made available by the DoD
High-Performance Computing Modernization Program. The
output is publicly available at https://hycom.org. We thank H-.
W. Kang and Y. S. Kim for their helpful advice and suggestions in
the project design and data analysis.
Data availability statement
The original contributions presented in the study are included
in the article/Supplementary Material. Further inquiries can be
directed to the corresponding author.
Conflict of interest
Author contributions
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
MH conceived of the study and performed all data analyses.
MH, YC, and JY contributed to the writing of the manuscript as well
as the conceptualization and interpretation of the results.
Publisher’s note
Funding
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations,
or those of the publisher, the editors and the reviewers. Any product
that may be evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the publisher.
This research was supported by Korea Institute of Marine
Science & Technology Promotion (KIMST) funded by the
Ministry of Oceans and Fisheries [(20220431, Development of
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